The immunodiagnostics: Traditional biomedical diagnostics have been structured around the analysis of a singular biomarker. The recent knowledge of the complexity and redundancy of protein networks and their changes during cancer progression suggest that the required sensitivity and reliability of such a diagnostics is possible only if a large number of biomarkers are quantitated concurrently. Such “panels” of biomarkers are needed to increase the specificity of the diagnostics. Even more importantly, most of these new biomarkers will be at much lower abundances than currently used markers, and many of them may be down regulated rather than up-regulated proteins. The limited sensitivity of prior-art immunoassays such as ELISA makes the use of additional cancer biomarkers difficult. More sensitive methods of biomarkers detection are required.
The diagnostic proteomics: It is too early to judge how many biomarkers should be used for any particular class of diseases. For example, in prostate or breast cancer, there are about 20 proteins whose abundance correlates with cancer. As such, the diagnostic panel(s) could be expected to feature about half of these putative biomarkers. However, one will need also to establish the immunologic status of a patient, e.g. quantitate 10-20 cytokines. Thus, one may need to quantitate up to 50 proteins concurrently. These type of studies are usually described as “diagnostic proteomics” and use the antibodies microarrays (P-chips).
As stated above, a majority of new biomarkers will be low abundance proteins. Thus, immunoassays with the highest sensitivity need to be used. The use of such super-sensitive immunoassays for the detection of markers will demand a considerable amount of biological material to be analyzed in an assay. A typical single immunoassay is performed in duplicate on a 200 microliter sample. With 32 samples, this leads to the need to obtain more than 10 ml of biological material, which in the biomedical applications is often difficult. Thus, the size of sample dedicated to a given target should be smaller (about 25 microliters). However, this leads to a need of an eight-fold higher assay sensitivity. For more than about 10 targets, the use of P-chips/MPD is critical and the need for high sensitivity is at a premium.
The need for improved sensitivity of immunoassays: Typically, the immunodiagnostic procedures are performed on relatively easily accessible physiologic fluids as blood, urine, saliva or breath condensate. In these situations it is possible to obtain a few milliliters of biological sample. However, there are also another situations, when only “forensic” sample of less than 10 microliter is available, e.g. in the case of needle biopsy.
The most important aspect of immunodiagnostics is the differentiation between the limit of detection (LOD) and limit of quantitation (LOQ). For assays such as ELISA, the limit of detection is typically a factor of few better than the limit of quantitation. Thus, for typical ELISA with LOD=1 pg/ml, reliable quantitation can be achieved in the range of 3-5 pg/ml. However, when the detection of proteins at 0.1 pg/ml is attempted, the ratio (LOD/LOQ) may considerably increase, i.e. at very low abundances quantitation becomes increasingly difficult. This can be traced to both the basic sources of non-specific biological backgrounds and the basic properties of rare processes when Poisson rather then Gaussian distributions are of importance.
The classical immunoassays such as ELISA achieved the 1 pg/ml sensitivity. A typical protein with the molecular weight (Mw) approximately equal to 20 kDaltons is equivalent to approximately 3×107 molecules or 5×10−17 Mole. For many years this sensitivity has been considered to be sufficient for almost all biomedical tasks. In recent years, however, many new challenges in biomedical diagnostics suggest that a few hundred-fold increase of sensitivity is required for optimal sensitive detection. The biomedical situations which justify such dramatic increases in the sensitivity of immunodiagnostics can be divided into several categories:                Detection of viral and bacterial pathogens with a few copies per milliliter of blood;        Detection of very low abundance of physiologically potent proteins, e.g. cytokines;        Detection of proteins across the physiologic barrier, e.g. in blood not in CSF and in breath condensate rather than in blood; and        Detection of rare, e.g. post-translationally modified form of the protein.        
In all these situations the detection sensitivity of a few hundred times better than prior-art immunoassays (such as ELISA) is necessary. They are illustrated in the “Applications” section below by examples of biomedical diagnostics tasks that require improved sensitivity. The main interests are in:                Obtaining reliable information about low abundance factors influencing the immune system, e.g. for autoimmune diseases;        Applications in oncology;        Ultrasensitive and low cost diagnostic of infectiouse diseases; and        Applications in neurodegenerative diseases.        
Two main trends in diagnostic proteomics: There are currently two main trends in diagnostic proteomics: extreme multiplexing vs. extreme sensitivity. With extreme multiplexing, a very large number of biomarkers are used but detection sensitivity is low. In currently existing antibody-based P-chips, information on about 100 biomarkers can be obtained with achievable limits of detection of about 5-10 pg/ml. In the “flow-cytometry derived” Luminex Inc. system, the sensitivity is between 10-50 pg/ml depending on the target. The reagents exist for about 100 targets, and a sub-set of about 50 is typically used for biological studies. In SELDI, the fragments of proteins can be detected at 100-200 pg/ml and then information about proteins can be indirectly obtained. SELDI has an advantage that not much information is required at the study initiation. However, as shown by the last 2-3 years of the diagnostic proteomics activities, the disadvantages of such diagnostic are lack of reliability, lack of comparison between groups, uncertain calibration, limited specificity and high cost.
According to the second school of thought, the detection of high abundance, mainly housekeeping proteins is a vestige of the past. The focus is now on an application tailored sub-set of low abundance biomarkers, typically the signaling proteins. In this case extremely high sensitivity is required. Previously it has been expected that this sensitivity's cut-off is at 0.5 pg/ml. my study showed the considerable advantage of supersensitive, i.e. down to 10 fg/ml, techniques. The ultimate sensitivity and optimal selection of biomarkers is used to compensate for the relatively low number of biomarkers.
The example: biomarkers for oncology: My studies show that detection of very low abundance proteins works well in detection of breast cancer, prostate cancer and melanoma. I evaluated this method using data from IA/MPD, Super-ELISA and a Luminex device. For optimal system, one needs to elucidate sensitivity and how many markers are needed. I evaluated the predictive power of different proteins as melanoma biomarkers based on the pioneering data of Dr. A. Lokshin, who used the Luminex-based measurement of over 70 biomarkers involving three cohorts: healthy individuals (n=44), pre-therapy melanoma patients (n=179) and post- therapy melanoma patients (n=172). For a number of important biomarkers, e.g. IL-1beta, IL-6, IL-2, TNFalpha, the Luminex devia is far from optimal. It can detect these biomarkers in less than 20% of healthy patients and less than 50% of melanoma patients. Also, a large number of outliers are observed.
FIG. 1 presents the averages over two cohorts for the above referenced 70 biomarkers. Note, that 90% of points are very close to the 45 degree line, i.e. have a very low predictive power when only averages are compared. Only seven biomarkers, all low abundance proteins, have a high predictive power. These are IL-6, IL-8, TNFalpha, VEGF, MP1alpha, MP1beta and MPA. I analyzed the distributions for these variables and demonstrated that IL-8 is the best single biomarker, with about 60% predictive power. Using prior-art methods of biostatistics and all 70 biomarkers, the detection sensitivity is at about 75%. Using my novel correlation-based method I achieved 90% sensitivity and specificity using only seven biomarkers. I expect, that when a more sensitive method is employed that has a better detection limit than Luminex, e.g. IA/MPD or Super-ELISA, the sensitivity and specificity can be further improved. The important point is that proteins with abundance above 50 pg/ml do not contribute significantly to the assays predictive power. On the other hand, a few carefully selected low abundance proteins provide reliable assay.
The tissue vs. systemic response biomarkers: Molecular diagnostics, especially as applied to oncology, is largely dependent on pathological information. Thus, the majority of biomarkers were discovered by differentially staining the tumors vs. healthy tissue. Only recently, the methods of discovery proteomics have been used to elucidate the differentially displayed proteins. Thus, the majority of available tumor markers are either a cytosolic or membrane proteins. Only a very small fraction of them are secreted proteins. These “tissue” biomarkers can be quite specific pathologically and yet almost useless when blood or other physiological fluids are used. There are strong barriers between the tissue and intersitial material and then between the tissue and blood. Thus, the abundance of tissue biomarkers in blood, e.g. serum, may vary greatly from patient to patient. Such tissue markers are organ and disease specific but are very difficult to quantitate because transfer through the barriers is a very complicated process.
Another class of biomarkers are the proteins which are involved in systemic response to disease. These can be modulators of immune response (cytokines), angiogenesis factors (AFs) or chemokines. There is a large sub-set of tumor infiltrating lymphocytes (TILs) that are chemo-attracted to tumors. These TILs, produce the cytokines or AFs inside the tumor. Locally, their concentration may be considerable, even if it is at a much lower level at blood. The person-to-person variation, however, is expected to be much lower than for tissue biomarkers. In a sense, cytokines/AFs/chemokines are “preferred” messengers and evolution permitted the development of many channels by which they efficiently propagate across the organism. For example, circulation of cytokines is not significantly attenuated by the brain-blood barrier. Obviously, these systemic response biomarkers are less specific than tissue biomarkers. The immune system evolved to respond by initiation of pro-inflammatory cascade of cytokines (IL-1beta, IL-6, TNFalpha) in case of microbial attack. The same mechanism is used to recognize/eliminate metastatic cells in cancer and is a crucial element of autoimmune cascade in asthma or arthritis. Thus, the measurement of a single cytokine is not very informative but an immunoprofiling can be specific.
The important aspects of using systemic response biomarkers is that they can be both down- and up-regulated. Furthermore, they can be strongly dependent on age and stage of disease. Finally, cytokines are the most potent signaling molecules. They are found in blood at sub-pg/ml levels and the ultimate sensitivity methods are necessary for their quantitation.
We implemented an innovative strategy of molecular diagnostics that synergistically used tissue and systemic response biomarkers. The systemic biomarkers can be divided into four sub-classes: pro-inflammatory cytokines, anti-inflammatory cytokines, angiogenesis factors and others. For each case, the quantitation of a plurality of factors is necessary for robust molecular diagnostics. Most important, all cytokines should be measured in all individuals. To achieve this, a group of novel, super-sensitive immunoassays (IA/MPD, Super-ELISA, RGIA) have been developed. The proposed method works only when a sensitivity better than 0.1 pg/ml is achieved. My group is the first worldwide to achieve this landmark sensitivity.
New methods of bioinformatics are necessary for analysis of biomarker patterns in oncology: Initially I expected that analysis of the data obtained using my super-sensitive methods of diagnostic proteomics would be possible using existing methods of biostatistics. Because my data shows that the distributions of almost all biomarkers are highly age-dependent and strongly non-Gaussian, I documented that the multi-dimensional correlations between biomarkers have a much higher predictive power than the distributions of any and each biomarker analyzed separately. We, therefore, developed a correlation based software to implement this new type of biostatistical analysis. This package contains the data input, calculations and sophisticated presentation software using wavelet and 3D modeling to facilitate the understanding of the correlations in obtained data sets. I believe that these programs may have many applications when any methods of immunodiagnostics and diagnostics proteomics are used, and that such a coordinated package of programs may be an important element of commercialization.